Automated Program Repair(APR)techniques have shown significant potential in mitigating the cost and complexity associated with debugging by automatically generating corrective patches for software defects.Despite cons...Automated Program Repair(APR)techniques have shown significant potential in mitigating the cost and complexity associated with debugging by automatically generating corrective patches for software defects.Despite considerable progress in APR methodologies,existing approaches frequently lack contextual awareness of runtime behaviors and structural intricacies inherent in buggy source code.In this paper,we propose a novel APR approach that integrates attention mechanisms within an autoencoder-based framework,explicitly utilizing structural code affinity and execution context correlation derived from stack trace analysis.Our approach begins with an innovative preprocessing pipeline,where code segments and stack traces are transformed into tokenized representations.Subsequently,the BM25 ranking algorithm is employed to quantitatively measure structural code affinity and execution context correlation,identifying syntactically and semantically analogous buggy code snippets and relevant runtime error contexts from extensive repositories.These extracted features are then encoded via an attention-enhanced autoencoder model,specifically designed to capture significant patterns and correlations essential for effective patch generation.To assess the efficacy and generalizability of our proposed method,we conducted rigorous experimental comparisons against DeepFix,a state-of-the-art APR system,using a substantial dataset comprising 53,478 studentdeveloped C programs.Experimental outcomes indicate that our model achieves a notable bug repair success rate of approximately 62.36%,representing a statistically significant performance improvement of over 6%compared to the baseline.Furthermore,a thorough K-fold cross-validation reinforced the consistency,robustness,and reliability of our method across diverse subsets of the dataset.Our findings present the critical advantage of integrating attentionbased learning with code structural and execution context features in APR tasks,leading to improved accuracy and practical applicability.Future work aims to extend the model’s applicability across different programming languages,systematically optimize hyperparameters,and explore alternative feature representation methods to further enhance debugging efficiency and effectiveness.展开更多
The authors regret Acknowledgements Firstly,the authors wish to acknowledge the academic support from Ruhr University Bochum during the first author's(Xiao Yan)research stay from 2018.11 to 2020.10,including the s...The authors regret Acknowledgements Firstly,the authors wish to acknowledge the academic support from Ruhr University Bochum during the first author's(Xiao Yan)research stay from 2018.11 to 2020.10,including the soft code implement and debug support from Vladislav Gudzulic and academic advising from Günther Meschke.展开更多
[Objective] The aim was to test the controlling effect of cleaning steriliza- tion system, material conveying system, and fermentation jar cooling system with equip- ments of fruit wine production line introduced in t...[Objective] The aim was to test the controlling effect of cleaning steriliza- tion system, material conveying system, and fermentation jar cooling system with equip- ments of fruit wine production line introduced in this study and its auto-control sys- tem field assembled and debugged. [Method] Based on controlling equipment and setting parameters on the configuration interface, the operation state of the control equipments could be real-time monitored and controlled with the help of configura- tion software. [Result] The result showed that the equipment system could reduce the temperature into 12 ℃ with the error of ＋0.5 ℃within 110 minutes when the fermentation temperature is set at 12 ℃ in real production. [Conclusion] The auto- control system of fruit wine production line was easy to be assembled and de- bugged to meet demands of different fruit wine productions.展开更多
文摘Automated Program Repair(APR)techniques have shown significant potential in mitigating the cost and complexity associated with debugging by automatically generating corrective patches for software defects.Despite considerable progress in APR methodologies,existing approaches frequently lack contextual awareness of runtime behaviors and structural intricacies inherent in buggy source code.In this paper,we propose a novel APR approach that integrates attention mechanisms within an autoencoder-based framework,explicitly utilizing structural code affinity and execution context correlation derived from stack trace analysis.Our approach begins with an innovative preprocessing pipeline,where code segments and stack traces are transformed into tokenized representations.Subsequently,the BM25 ranking algorithm is employed to quantitatively measure structural code affinity and execution context correlation,identifying syntactically and semantically analogous buggy code snippets and relevant runtime error contexts from extensive repositories.These extracted features are then encoded via an attention-enhanced autoencoder model,specifically designed to capture significant patterns and correlations essential for effective patch generation.To assess the efficacy and generalizability of our proposed method,we conducted rigorous experimental comparisons against DeepFix,a state-of-the-art APR system,using a substantial dataset comprising 53,478 studentdeveloped C programs.Experimental outcomes indicate that our model achieves a notable bug repair success rate of approximately 62.36%,representing a statistically significant performance improvement of over 6%compared to the baseline.Furthermore,a thorough K-fold cross-validation reinforced the consistency,robustness,and reliability of our method across diverse subsets of the dataset.Our findings present the critical advantage of integrating attentionbased learning with code structural and execution context features in APR tasks,leading to improved accuracy and practical applicability.Future work aims to extend the model’s applicability across different programming languages,systematically optimize hyperparameters,and explore alternative feature representation methods to further enhance debugging efficiency and effectiveness.
文摘The authors regret Acknowledgements Firstly,the authors wish to acknowledge the academic support from Ruhr University Bochum during the first author's(Xiao Yan)research stay from 2018.11 to 2020.10,including the soft code implement and debug support from Vladislav Gudzulic and academic advising from Günther Meschke.
基金Supported by Fundamental Research Foundation of GXAAS(GNK2013YM02)~~
文摘[Objective] The aim was to test the controlling effect of cleaning steriliza- tion system, material conveying system, and fermentation jar cooling system with equip- ments of fruit wine production line introduced in this study and its auto-control sys- tem field assembled and debugged. [Method] Based on controlling equipment and setting parameters on the configuration interface, the operation state of the control equipments could be real-time monitored and controlled with the help of configura- tion software. [Result] The result showed that the equipment system could reduce the temperature into 12 ℃ with the error of ＋0.5 ℃within 110 minutes when the fermentation temperature is set at 12 ℃ in real production. [Conclusion] The auto- control system of fruit wine production line was easy to be assembled and de- bugged to meet demands of different fruit wine productions.
